Obtaining data linkage consent for children: factors influencing outcomes and potential biases

Understanding factors associated with consent for data linkage has largely focussed on adults, but parents or guardians can also be asked to consent on behalf of children for whom they are responsible. A framework for consent decision is presented, and is tested using a large nationally representative survey asking mothers to consent for both themselves and their children for two sets of records. Nearly all mothers give the same consent outcome for all their children. Consent rates are higher for education records than for health records and higher for mothers than children. Multivariate analyses suggest that minorities are generally less likely to consent, while more trust increases chances of consent. Several survey environment factors are important, with harder-to-contact respondents less likely to consent, while the presence of others and higher interviewer-respondent rapport lead to a higher chance of consent. These findings suggest potential methodologies to improve consent rates and possibly minimise bias. This is important given significant demographic differences between children across consent outcomes. However, data from a survey of 10?15 year olds in the study shows fewer differences for several important behaviours and attitudes across consent outcomes

Propensity to consent to data linkage: experimental evidence on the role of three survey design features in a UK longitudinal panel

When performing data linkage, survey respondents need to provide their informed consent. Since not all respondents agree to this request, the linked data-set will have fewer observations than the survey data-set alone and bias may be introduced. By focusing on the role that survey design features play in gaining respondents’ consent, this paper provides an innovative contribution to the studies in this field. Analysing experimental data collected in a nationally representative household panel survey of the British population, we find that interview features such as question format (dependent/independent questions) and placement of the consent question within the questionnaire have an impact on consent rates

The impact of using the Web in a mixed-mode follow-up of a longitudinal birth cohort study: Evidence from the National Child Development Study

A sequential mixed-mode data collection, online-to-telephone, was introduced into the National Child Development Study for the first time at the study's age 55 sweep in 2013. The study included a small experiment, whereby a randomised subset of study members was allocated to a single mode, telephone-only interview, in order to test for the presence of mode effects on participation and measurement. Relative to telephone-only, the offer of the Web increased overall participation rates by 5.0 percentage points (82.8% vs. 77.8%; 95% confidence interval for difference: 2.7% to 7.3%). Differences attributable to mode of interview were detected in levels of item non-response and response values for a limited number of questions. Most notably, response by Web (relative to telephone) was found to have increased the likelihood of non-response to questions relating to pay and other financial matters, and increased the likelihood of ‘less desirable’ responses. For example, response by Web resulted in the reporting of more units of alcohol consumed, and more negative responses to subjective questions such as self-rated health, self-rated financial status and well-being. As there was evidence of mode effects, there is the potential for biases in some analyses, unless appropriate techniques are utilised to correct for these

A data-driven approach to understanding non-response and restoring sample representativeness in the UK Next Steps cohort

Non-response is common in longitudinal surveys, reducing efficiency and introducing the potential for bias. Principled methods, such as multiple imputation, are generally required to obtain unbiased estimates in surveys subject to missingness which is not completely at random. The inclusion of predictors of non-response in such methods, for example as auxiliary variables in multiple imputation, can help improve the plausibility of the missing at random assumption underlying these methods and hence reduce bias. We present a systematic data-driven approach used to identify predictors of non-response at Wave 8 (age 25–26) of Next Steps, a UK national cohort study that follows a sample of 15,770 young people from age 13–14 years. The identified predictors of non-response were across a number of broad categories, including personal characteristics, schooling and behaviour in school, activities and behaviour outside of school, mental health and well-being, socio-economic status, and practicalities around contact and survey completion. We found that including these predictors of non-response as auxiliary variables in multiple imputation analyses allowed us to restore sample representativeness in several different settings, though we acknowledge that this is unlikely to universally be the case. We propose that these variables are considered for inclusion in future analyses using principled methods to explore and attempt to reduce bias due to non-response in Next Steps. Our data-driven approach to this issue could also be used as a model for investigations in other longitudinal studies

Correlates of record linkage and estimating risks of non-linkage biases in business data sets

Researchers often utilize data sets that link information from multiple sources, but non‐linkage biases caused by linked and non‐linked subject differences are little understood, especially in business data sets. We address these knowledge gaps by studying biases in linkable 2010 UK Small Business Survey data sets. We identify correlates of business linkage propensity, and also for the first time its components: consent to linkage and register identifier appendability. As well, we take a novel approach to evaluating non‐linkage bias risks, by computing data set representativeness indicators (comparable, decomposable sample subset similarity measures). We find that the main impacts on linkage propensities and bias risks are due to consenter–non‐consenter differences explicable given business survey response processes, and differences between subjects with and without identifiers caused by register undercoverage of very small businesses. We then discuss consequences for the analysis of linked business data sets, and implications of the evaluation methods we introduce for linked data set producers and users

Linking Twitter and Survey Data: The Impact of Survey Mode and Demographics on Consent Rates Across Three UK Studies.

In light of issues such as increasing unit nonresponse in surveys, several studies argue that social media sources such as Twitter can be used as a viable alternative. However, there are also a number of shortcomings with Twitter data such as questions about its representativeness of the wider population and the inability to validate whose data you are collecting. A useful way forward could be to combine survey and Twitter data to supplement and improve both. To do so, consent within a survey is first needed. This study explores the consent decisions in three large representative surveys of the adult British population to link Twitter data to survey responses and the impact that demographics and survey mode have on these outcomes. Findings suggest that consent rates for data linkage are relatively low, and this is in part mediated by mode, where face-to-face surveys have higher consent rates than web versions. These findings are important to understand the potential for linking Twitter and survey data but also to the consent literature generally

Small Area Estimation of Latent Economic Well-being

© The Author(s) 2019. Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of well-being, for example, policy makers need detailed information about the geographical distribution of a range of social indicators. We investigate data dimensionality reduction using factor analysis models and implement SAE on the factor scores under the empirical best linear unbiased prediction approach. We contrast this approach with the standard approach of providing a dashboard of indicators or a weighted average of indicators at the local level. We demonstrate the approach in a simulation study and a real data application based on the European Union Statistics for Income and Living Conditions for the municipalities of Tuscany

Seasonal and annual fluxes of nutrients and organic matter from large rivers to the Arctic Ocean and surrounding seas

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Estuaries and Coasts 35 (2012): 369-382, doi:10.1007/s12237-011-9386-6.River inputs of nutrients and organic matter impact the biogeochemistry of arctic estuaries and the Arctic Ocean as a whole, yet there is considerable uncertainty about the magnitude of fluvial fluxes at the pan-arctic scale. Samples from the six largest arctic rivers, with a combined watershed area of 11.3 x 106 km2, have revealed strong seasonal variations in constituent concentrations and fluxes within rivers as well as large differences among the rivers. Specifically, we investigate fluxes of dissolved organic carbon, dissolved organic nitrogen, total dissolved phosphorus, dissolved inorganic nitrogen, nitrate, and silica. This is the first time that seasonal and annual constituent fluxes have been determined using consistent sampling and analytical methods at the pan arctic scale, and consequently provide the best available estimates for constituent flux from land to the Arctic Ocean and surrounding seas. Given the large inputs of river water to the relatively small Arctic Ocean, and the dramatic impacts that climate change is having in the Arctic, it is particularly urgent that we establish the contemporary river fluxes so that we will be able to detect future changes and evaluate the impact of the changes on the biogeochemistry of the receiving coastal and ocean systems.This work was supported by the National Science Foundation through grants OPP-0229302, OPP-0519840, OPP-0732522, and OPP-0732944. Additional support was provided by the U. S. Geological Survey (Yukon River) and the Department of Indian and Northern Affairs (Mackenzie River)